The most challenge is that there are large quantities of redundant shapelets in candidates which decreasing the accuracy of classification and the parameter k is hard to determine. Some works [1]}, [2]} detect this problem and use clustering or pruning methods to remove the redundant,but still exist redundant shapelets, also, the k value is determined from experiments. This paper make the following contributions: First, in order to get rid of the similar and redundant shapelets in candidate set, two conceptions including similar shapelets and diversified top-k shapelets are presented. Based on these conceptions, a method of construction diversify shapelets graph is proposed. Second, a diversified top-k shapelets query method is presented to find top-k representative shapeletes of each class. Third, we propose an diversified top-k shapelets transformed ELM algorithm which can automatically determine the parameter k and transform data using the determined k shapelets. The experimental results show that the proposed approach significantly improves the interpretability and performance of ELM.
